What GPU do I need to run nvidia/NVIDIA-Nemotron-Nano-9B-v2?
8.9B parameters, published in BF16. View on Hugging Face
NVIDIA-Nemotron-Nano-9B-v2 is published by nvidia on Hugging Face, with 301,423 downloads and 506 likes to date. It's a NemotronHForCausalLM model built for text-generation, published natively in BF16.
VRAM required & cheapest live GPU fit
Required VRAM = weight size at each precision, plus a fixed overhead for KV-cache, activations, and fragmentation. Full formula and assumptions: methodology.
A GPU is only matched to a row if its hardware supports that precision, and the primary recommendation is always a single-GPU fit when one exists.
INT4 caveat: requires a quantized checkpoint actually published for this model — check its Hugging Face page before relying on this row.
Cheapest way to run NVIDIA-Nemotron-Nano-9B-v2 at its published (BF16) precision: 1× RTX 3090 on simplepod, at $0.160/hr per GPU ($0.160/hr total). Quantizing to FP8 or INT4 (rows above) can cost less, but requires a compatible quantized checkpoint to exist for this model.
Weight-to-VRAM math, the fit rules, and how live prices are normalized: full methodology.
More nvidia models
- Gemma-4-31B-IT-NVFP4 (20.9B, BF16)
- NVIDIA-Nemotron-3-Nano-30B-A3B-BF16 (31.6B, BF16)
- NVIDIA-Nemotron-3-Super-120B-A12B-BF16 (123.6B, BF16)
- NVIDIA-Nemotron-3-Nano-4B-BF16 (4.0B, BF16)
- NVIDIA-Nemotron-3-Nano-30B-A3B-FP8 (31.6B, F8_E4M3)
- Nemotron-Labs-Diffusion-8B-Base (8.5B, BF16)
